{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from os import walk\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import pylab\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib\n",
    "import cv2\n",
    "from sklearn.decomposition import PCA\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CHANNEL_NUM = 3\n",
    "pixel_num = 0\n",
    "x = []\n",
    "channel_sum = np.zeros(CHANNEL_NUM)\n",
    "for file in f:\n",
    "    image = cv2.imread(os.path.join(path, file))\n",
    "    x = np.concatenate([np.asarray(image)])\n",
    "\n",
    "train_mean = np.mean(x, axis=(0, 1))\n",
    "sum_1 = np.sum(train_mean)\n",
    "\n",
    "print(train_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "path = \"/home/pf/pfshare/data/MA_Rajanie/Data/Lake_Ice_Dataset_16-17/Moritz/aug/aug_raw\"\n",
    "f = []\n",
    "for (dirpath, dirnames, filenames) in walk(path):\n",
    "    f.extend(filenames)\n",
    "    break\n",
    "# print(f)\n",
    "# print(f_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "background = 0\n",
    "water = 0\n",
    "ice = 0\n",
    "snow = 0\n",
    "clutter = 0\n",
    "labels = []\n",
    "for file in f:\n",
    "    im = Image.open(os.path.join(path, file), \"r\")\n",
    "    pix_val = list(im.getdata())\n",
    "    for pixel in pix_val:\n",
    "        if pixel == 0:\n",
    "            background += 1\n",
    "            labels.append(0)\n",
    "        elif pixel == 1:\n",
    "            water +=1 \n",
    "            labels.append(1)\n",
    "        elif pixel == 2:\n",
    "            ice += 1\n",
    "            labels.append(2)\n",
    "        elif pixel == 3:\n",
    "            snow += 1\n",
    "            labels.append(3)\n",
    "        elif pixel == 4:\n",
    "            clutter += 1\n",
    "            labels.append(4)\n",
    "y = np.asarray(labels)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = background + water + ice + snow + clutter\n",
    "\n",
    "\n",
    "print(\"Percentage of pixels which belong to background: \",background/total)\n",
    "print(\"Percentage of pixels which belong to water:      \",water/total)\n",
    "print(\"Percentage of pixels which belong to ice:        \",ice/total)\n",
    "print(\"Percentage of pixels which belong to snow:       \",snow/total)\n",
    "print(\"Percentage of pixels which belong to clutter:    \",clutter/total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cam0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kwargs = dict(alpha=0.5, bins=100)\n",
    "\n",
    "plt.hist(labels, label=\"Cam0\")\n",
    "plt.hist(labels_1, label=\"Cam1\")\n",
    "plt.gca().set(title='Frequency Histogram of Lake Ice classes', ylabel='Frequency')\n",
    "plt.xlim(0,5)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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